@inproceedings{wang-etal-2022-nycu,
    title = "{NYCU}{\_}{TWD}@{LT}-{EDI}-{ACL}2022: Ensemble Models with {VADER} and Contrastive Learning for Detecting Signs of Depression from Social Media",
    author = "Wang, Wei-Yao  and
      Tang, Yu-Chien  and
      Du, Wei-Wei  and
      Peng, Wen-Chih",
    editor = "Chakravarthi, Bharathi Raja  and
      Bharathi, B  and
      McCrae, John P  and
      Zarrouk, Manel  and
      Bali, Kalika  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.ltedi-1.15/",
    doi = "10.18653/v1/2022.ltedi-1.15",
    pages = "136--139",
    abstract = "This paper presents a state-of-the-art solution to the LT-EDI-ACL 2022 Task 4: \textit{Detecting Signs of Depression from Social Media Text}. The goal of this task is to detect the severity levels of depression of people from social media posts, where people often share their feelings on a daily basis. To detect the signs of depression, we propose a framework with pre-trained language models using rich information instead of training from scratch, gradient boosting and deep learning models for modeling various aspects, and supervised contrastive learning for the generalization ability. Moreover, ensemble techniques are also employed in consideration of the different advantages of each method. Experiments show that our framework achieves a 2nd prize ranking with a macro F1-score of 0.552, showing the effectiveness and robustness of our approach."
}